A blog for the cryptography group of the University of Bristol. To enable discussion on cryptography and other matters related to our research.

Tuesday, May 2, 2017

Eurocrypt 2017: On dual lattice attacks against small-secret LWE and parameter choices in HElib and SEAL

This morning, Martin gave a great talk on lattice attacks and parameter choices for Learning With Errors (LWE) with small and sparse secret. The work presents new attacks on LWE instances, yielding revised security estimates. This leads to a revised exponent of the dual lattice attack by a factor of 2L/(2L+1), for log q = Θ(L*log n). The paper exploits the fact that most lattice-based FHE schemes use short and sparse secret. We will write q to denote the LWE modulus throughout.

Let's first have a look at the set-up. Remember LWE consists of distinguishing between pairs (A, As+e) and (A,b). In the first instance, A is selected uniformly at random and b is selected from a special (usually Gaussian) distribution. In the second one, both A and b are uniformly random. Selecting s, as this work shows, is perhaps trickier than previously thought. Theory says that, in order to preserve security, selecting a short and sparse secret s means the dimension must be increased to n*log_2(q). Practice says just ignore that and pick a small secret anyway. More formally, HElib typically picks a secret s such that exactly h=64 entries are in {-1,1} and all the rest are 0. SEAL picks uniformly random secrets in {-1,0,1}.

We also recall that the dual lattice attack consists of finding a short vector w such that Aw = 0, then checking if

<Aw, (As+e)w> = <w,e>

is short. If we are in the presence of an LWE sample, e is short, so the inner product is short. Short*short = short, as any good cryptographer can tell you.

The improvements presented in this paper rely on three main observations. Firsly, a revised dual lattice attack is presented. This step is done by adapting BKW-style algorithms in order to increase efficiency and can be done in general, i.e. does not depend on either shortness or sparseness of the secret. It is achieved by applying BKZ to the target basis, then re-randomising the result and applying BKZ again, with different block size.

The second optimisation exploits the fact that we have small secrets. We observe that we can relax the condition on w somewhat. Indeed, if s is short, then finding w such that Aw is short instead of 0 is good enough. Therefore, we look for vectors (v,w) in the lattice

L = {(y,x): yA = x (mod q)}.

Now in small secret LWE instances, ||s||<||e|| and so we may allow ||v||>||w|| such that

||<w,s>|| ≈ ||<v,e>||.

Finally, the sparsity of the small secret is exploited. This essentially relies on the following observation: when s is very sparse, most of the columns of A become irrelevant, so we can just ignore them.

The final algorithm SILKE is the combination of the three above steps. The steps are the following.

Perform BKZ twice with different block sizes to produce many short vectors